For training and evaluation of our models we used six different speech datasets that have been force-aligned with a phonetic transcription. Models are trained on the speech only and phonetic force-alignments are used for evaluation metrics.
First, we use the LibriSpeech dataset [1]} composed of a 960 hours training set and four 5 hours held-out datasets for development and test called: dev-clean, base at test-clean and test-other. Then, to study generalization, we used five speech corpora from the Zerospeech challenges [2]}: English (45hours), Xitsonga (2h30), Mandarin (2h30), French (24h) and the Buckeye (5 hours of conversational English). As these corpora do not have a held-out test set, evaluation is performed using the training set, a common practice in unsupervised speech processing [2]}.
